The libcubwt is a library for fast (see Benchmarks below) GPU-based Burrows-Wheeler transform construction and inversion on the CUDA platform using prefix doubling + Skew/DC3 approach as described in the following papers:

• Vitaly Osipov, Parallel Suffix Array Construction for Shared Memory Architectures, 2012
• Leyuan Wang, Sean Baxter, and John D. Owens Fast Parallel Suffix Array on the GPU, 2015
• Florian Büren, Daniel Jünger, Robin Kobus, Christian Hundt, Bertil Schmidt Suffix Array Construction on Multi-GPU Systems, 2019

Copyright (c) 2022-2024 Ilya Grebnov ilya.grebnov@gmail.com

The libcubwt provides simple API to construct Burrows-Wheeler transform from a given string over constant-size alphabet using 20.5n bytes of GPU memory.

• The libcubwt works with any CUDA compatible GPU, but I recommend SM 8.9+ Ada Lovelace (CUDA 11.8 and later) due to very large L2 cache.
• The libcubwt is sensitive to fast GPU memory and might not be suitable for some workloads. Please benchmark yourself.

• Starting with version 1.5.0, libcubwt algorithm was changed and no longer supports the computation of suffix arrays or inverse suffix arrays. To compute these arrays, please consider using a version prior to 1.5.0.

The libcubwt is released under the Apache License Version 2.0 and is considered suitable for production use. However, no warranty or fitness for a particular purpose is expressed or implied.

• January 24, 2024 (1.6.0)
  • Inverse Burrows-Wheeler transform.
• March 24, 2023 (1.5.0)
  • Reduced memory usage and improved performance.
• February 10, 2023 (1.0.0)
  • Initial public release of the libcubwt.

    /**
    * Allocates storage on the CUDA device that allows reusing allocated memory with each libcubwt operation.
    * @param max_length The maximum length of string to support.
    * @return LIBCUBWT_NO_ERROR if no error occurred, libcubwt error code otherwise.
    */
    int64_t libcubwt_allocate_device_storage(void ** device_storage, int64_t max_length);

    /**
    * Destroys the previously allocated storage on the CUDA device.
    * @param device_storage The previously allocated storage on the CUDA device.
    * @return LIBCUBWT_NO_ERROR if no error occurred, libcubwt error code otherwise.
    */
    int64_t libcubwt_free_device_storage(void * device_storage);

    /**
    * Constructs the Burrows-Wheeler Transform (BWT) of a given string.
    * @param device_storage The previously allocated storage on the CUDA device.
    * @param T [0..n-1] The input string.
    * @param L [0..n-1] The output string (can be T).
    * @param n The length of the input string.
    * @return The primary index if no error occurred, libcubwt error code otherwise.
    */
    int64_t libcubwt_bwt(void * device_storage, const uint8_t * T, uint8_t * L, int64_t n);

    /**
    * Reconstructs the original string from a given burrows-wheeler transformed string (BWT) with primary index.
    * @param device_storage The previously allocated storage on the CUDA device.
    * @param T [0..n-1] The input string.
    * @param U [0..n-1] The output string (can be T).
    * @param n The length of the given string.
    * @param freq [0..255] The input symbol frequency table (can be NULL).
    * @param i The primary index.
    * @return LIBCUBWT_NO_ERROR if no error occurred, libcubwt error code otherwise.
    */
    int64_t libcubwt_unbwt(void * device_storage, const uint8_t * T, uint8_t * U, int64_t n, const int32_t * freq, int32_t i);

• Input files were capped at 352MB due to GPU memory limit.
• All timings exclude initialization and memory allocations. However, the time for data transfer in and out of GPU is included.
• The timings are minimum of five runs measuring multi-threading performance of Burrows-Wheeler transform construction.

• OS: Microsoft Windows 10 Pro 64-Bit
• MB: MSI MPG Z390M GAMING EDGE AC, PCIe 3.0 x16
• CPU: Intel Core i7-9700K Processor (12M Cache, 5GHz all cores)
• RAM: 2x8 GB dual-channel DDR4 (4133 MHz, 17-17-17-37)
• GPU: ASUS TUF Gaming GeForce RTX 4070 Ti 12GB GDDR6X OC Edition
• Compiler: Microsoft Visual C++ compiler v19.34.31937 for x64
• Optimizations (CPU): /MD /DNDEBUG /O2 /GL /arch:AVX2 /openmp
• Optimizations (GPU): -arch=native --use_fast_math